Qian Kai, Feng Qiang, Wang Jia-Rui, Zhu Jia-De, Wang Ping, Guo Yu, Zhou Tao, Zhu Qian-Wei, Cai Liao, Zhang Zheng, He Gong-Hao
Department of Clinical Pharmacy, 920th Hospital of Joint Logistics Support Force of People's Liberation Army, Kunming, China.
College of Pharmacy, Dali University, Dali, China.
Discov Oncol. 2024 Nov 19;15(1):680. doi: 10.1007/s12672-024-01563-3.
Anaplastic thyroid carcinoma (ATC) is a rare but the most aggressive type of thyroid carcinoma. Nevertheless, limited advances were made to reduce mortality and improve survival over the last decades. Therefore, identifying novel diagnostic biomarkers and therapeutic targets for ATC patients is still needed.
RNA sequencing data and corresponding clinical features were available from GEO and TCGA databases. We integrated WGCNA and PPI network analysis to identify hub genes associated with ATC development, and RT-qPCR was employed for data verification. Univariate and LASSO Cox regression analyses were used to generate prognostic signatures.
Based on PPI and WGCNA, 6 hub genes were identified, namely KIF2C, PBK, TOP2A, CDK1, KIF20A, and ASPM, which play vital roles in ATC development. Subsequently, RT-qPCR experiments showed that most of these genes were significantly upregulated in CAL-62 cells compared to Nthy-ori 3-1 cells. Moreover, a prognostic signature featuring GPSM2, FGF5, ASXL3, CYP4B1, CLMP, and DUXAP9 was generated, which was also verified by RT-qPCR results and proved as an independent predictor of poorer prognosis of ATC. Additionally, a nomogram incorporating the risk score and clinicopathological parameters was further constructed for accurate prediction of 1-, 3- and 5-year survival probabilities of ATC.
Our study identified 6 key genes critical to ATC development and constructed a prognostic signature. These findings provide reliable biomarkers and a relatively comprehensive tumorigenesis profile of ATC, which may inform future strategies for clinical diagnosis and pharmaceutical design.
间变性甲状腺癌（ATC）是一种罕见但侵袭性最强的甲状腺癌类型。然而，在过去几十年中，降低死亡率和提高生存率方面进展有限。因此，仍需要为ATC患者识别新的诊断生物标志物和治疗靶点。
从GEO和TCGA数据库获取RNA测序数据及相应临床特征。我们整合加权基因共表达网络分析（WGCNA）和蛋白质-蛋白质相互作用（PPI）网络分析来识别与ATC发生相关的枢纽基因，并采用逆转录定量聚合酶链反应（RT-qPCR）进行数据验证。使用单因素和LASSO Cox回归分析生成预后特征。
基于PPI和WGCNA，鉴定出6个枢纽基因，即KIF2C、PBK、TOP2A、CDK1、KIF20A和ASPM，它们在ATC发生中起关键作用。随后，RT-qPCR实验表明，与Nthy-ori 3-1细胞相比，这些基因中的大多数在CAL-62细胞中显著上调。此外，生成了一个以GPSM2、FGF5、ASXL3、CYP4B1、CLMP和DUXAP9为特征的预后特征，RT-qPCR结果也对其进行了验证，并证明其为ATC预后较差的独立预测指标。此外，还进一步构建了一个包含风险评分和临床病理参数的列线图，用于准确预测ATC患者1年、3年和5年的生存概率。
我们的研究鉴定出6个对ATC发生至关重要的关键基因，并构建了一个预后特征。这些发现提供了可靠的生物标志物以及相对全面的ATC肿瘤发生概况，可能为未来的临床诊断策略和药物设计提供参考。
